Rethink AI-based Power Grid Control: Diving Into Algorithm Design
This paper addresses the problem of effective and robust voltage control in power grids for grid operators, offering an incremental improvement over existing RL methods.
This paper tackles the problem of voltage control in power grids using AI. The authors propose an imitation learning-based approach that directly maps power grid operating points to actions, achieving strong generalization ability with less training time and outperforming previous RL agents.
Recently, deep reinforcement learning (DRL)-based approach has shown promisein solving complex decision and control problems in power engineering domain.In this paper, we present an in-depth analysis of DRL-based voltage control fromaspects of algorithm selection, state space representation, and reward engineering.To resolve observed issues, we propose a novel imitation learning-based approachto directly map power grid operating points to effective actions without any interimreinforcement learning process. The performance results demonstrate that theproposed approach has strong generalization ability with much less training time.The agent trained by imitation learning is effective and robust to solve voltagecontrol problem and outperforms the former RL agents.